Wood naturally contains moisture. However, for wood lumber to be useful for building, the moisture content must fall within certain constraints. Too much or too little water in the lumber can cause serious problems in the subsequent processing and use of the lumber, including warping or difficulties with painting or gluing. In order to provide a useful product, most lumber mills dry their lumber under controlled processes, such as in a kiln, prior to sale. It is difficult, however, to ensure that every board in a given charge will have exactly the same moisture content after drying.
Part of the problem lies in the nature of the wood itself; wood is a non-homogenous material that does not dry at a constant rate. Even within a charge with all boards of the same size and same type of wood there are many factors that can affect drying rate. Individual different boards may have natural variability in their drying rate, may start out with different moisture content, may be composed of different percentages of sapwood and hardwood, or may contain wet pockets. Moisture variations can also result from variable drying conditions between different kilns at the same mill or within a single kiln charge due to the locations of particular boards within a kiln. While the variation between charges or kilns can be monitored using averages, the variability within a charge is best monitored using standard deviations. One method by which this monitoring can be done is by using statistical process control (SPC).
SPC works to continuously improve the quality of a process through the constant application of statistical methods to data produced at various stages of the process. Ideally, disturbances within the process are quickly discerned, then, using the data set, causes are assigned to those disturbances. In the best case, this is done with sufficient speed that investigation of the problem, diagnosis, and corrective action can be taken before many noncomforming products reach the final state of the process. This is usually done through the use of parametric control charts for variables. Generally, by plotting the average and standard deviation of multiple samples on a control chart and comparing those values against computed acceptable limits, the chart can be used to determine if a process is going out of control.
The starting point for SPC analysis is an understanding of the underlying distribution of the data under analysis. The conventional assumption has been that wood moisture content data is approximately Normally distributed. However, wood is a non-homogenous material, and even when it is kiln-dried under controlled conditions, the moisture content retains large variability through the wood pieces, so a board with an acceptable average rating may be quite dry in one section and quite wet in another. Additionally, because kiln-dried wood cannot be dried past the point at which it reaches equilibrium with the air in the kiln, there is an effective lower limit on moisture content. Thus, a Normal distribution has proven less than ideal for SPC analysis of wood drying and has made the task of creating a proper model for SPC analysis of wood drying difficult. It is desirable to design control charts specifically to monitor wood-drying, using a better-performing distribution model, if possible.
The theory of variability is the basis for the construction and use of control charts. The purpose of any monitoring process is to sort out meaningful signals from background noise. A certain amount of variability in the moisture content of the dried lumber—background noise—exists in the kiln-drying process. Boards dry at different rates due to inherent properties in the wood itself, as mentioned above. This natural variability can be considered a system of “common causes.” A drying process that is operating with only common causes of variation is considered to be in statistical control.
A major objective of the control charts is to sort out a meaningful signal from the background noise. When the moisture variability of the dried wood exceeds the background noise variability, this might indicate that there are problems associated with the kiln itself. This variability in moisture content usually arises from three sources: improperly adjusted kiln equipment, operator errors, or defective raw material. Such variability may be large when compared to the background noise, and it usually represents an unacceptable level of process performance. These sources of variability are called “assignable causes.” A process that is operating in the presence of such causes is said to be “out of control.” A kiln can operate in the “in control” state for a long time, producing acceptable moisture content levels in lumber. Eventually, however, assignable causes will occur, resulting in a shift to an out of control state, where the lumber produced no longer conforms to requirements.
A major objective of the control charts is to quickly detect the occurrences, or the trends toward assignable causes of process shifts, which allows investigation into causes, and corrective action to be undertaken before many nonconforming units are produced. Control charts can also be used to estimate the parameters of the kiln-drying process, to determine process capability, and to incrementally improve the process. In general, control charts are an effective tool that can be used to reduce process variability.
Many quality control methods suggested in the literature are concerned with the average moisture content alone, and therefore use methods such as sample estimation, or go/no go decision criteria, such as acceptance sampling. Other methods have been tried that do concern themselves with variation, but are based on the assumption that moisture content has a Normal probability distribution. This assumption tends to erroneously consider certain in-control conditions as defects or out-of-control conditions. Furthermore, assumptions of Normality underlie many other common statistics, such as correlation coefficients, t-tests, and others that may be used in standard statistical analysis. The failure to understand that a distribution is not Normal leads to a rippling statistical effect, with invalid assumptions producing incorrect answers which are themselves imperfectly analyzed, and so on.
Lognormal, rather than Normal, probability plotting for kiln analysis has been proposed by “Moisture Control During Kiln Drying,” McMahon, 1961, in conjunction with frequency distribution analysis and quality control methods. These techniques were used to estimate the average moisture content and the percentage of moisture content outside of certain boundaries. McMahon's methods were only approximate, however and introduced procedural errors that were potentially large. Additionally, McMahon's technique failed to properly account for the lower moisture content limit of kiln-dried wood. What is needed is a method that accounts for the lower moisture content limit that kiln-dried wood approaches and monitors kiln conditions more closely.